2 citations · 3 across the 5 of their papers we have counts for
6 papers
DeRA: Decoupled Representation Alignment for Video Tokenization
Pengbo Guo, Junke Wang, Zhen Xing +4
This paper presents DeRA, a novel 1D video tokenizer that decouples the spatial-temporal representation learning in video tokenization to achieve better training efficiency and per…
Learning Deblurring Texture Prior from Unpaired Data with Diffusion Model
Chengxu Liu, Lu Qi, Jinshan Pan +2
Since acquiring large amounts of realistic blurry-sharp image pairs is difficult and expensive, learning blind image deblurring from unpaired data is a more practical and promising…
Frequency Domain-Based Diffusion Model for Unpaired Image Dehazing
Chengxu Liu, Lu Qi, Jinshan Pan +2
Unpaired image dehazing has attracted increasing attention due to its flexible data requirements during model training. Dominant methods based on contrastive learning not only intr…
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution
Yuanting Fan, Chengxu Liu, Nengzhong Yin +2
Diffusion models (DMs) have shown promising results on single-image super-resolution and other image-to-image translation tasks. Benefiting from more computational resources and lo…
Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring
Chengxu Liu, Xuan Wang, Xiangyu Xu +4
Eliminating image blur produced by various kinds of motion has been a challenging problem. Dominant approaches rely heavily on model capacity to remove blurring by reconstructing r…
Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display Camera
Chengxu Liu, Xuan Wang, Yuanting Fan +2
Under-display camera (UDC) systems are the foundation of full-screen display devices in which the lens mounts under the display. The pixel array of light-emitting diodes used for d…